R Notebook

Dieses Notebook bereitet die Daten für die Intelligent Zoning Engine vor. Es speichert

• entities.geojson — Schulen, deren Geokoordinaten und Attribute: entity_id, capacity, und andere Attribute
• entities.csv — Statistische Blöcke Berlins und optimierungsrelevante Attribute: entity_id, capacity
• units.geojson — Statistische Blöcke Berlins, deren Geometrie und Attribute
• units.csv — Statsitische Blöcke Berlins und optimierungsrelevante Attribute: unit_id, population, percentage_sgb
• weights.csv — optimierungsrelevante Gewichte wie Fußwege / Spalten: entity_id, unit_id, weight, value
• assignment.csv — eine initiale Zuordnung / Spalten: unit_id, entity_id

Bereits in anderen scripten wurde vorbereitet:

• Fußwegen von einer großen Sichprobe von (Wohn-)Gebäuden zu allen Schulen wurden berechnet und in route_matrix.csv gespeichert
• Die Stichprobe wurde in sampled_buildings.csv gespeichert

Die Daten werden wiefolgt vorbereitet:

• pro Block werden die Anzahl der einzuschulenden Kinder mit Hilfe der Einwohnerzahlen nach Alter auf LOR-Ebene in EWR201512E_Matrix.csv hochgerechnet
  • Kinder des LOR werden Anteilig nach Einwohnerzahl des Blocks im Verhältnis zum LOR auf die Blöcke verteilt
• es werden minimale, durchschnittliche und maximale Fußwege aus jedem Block errechnet

TODO: - die sozioökonomischen Faktoren werden aus den Wahlbezirken auf die Blöcke hochgerechnet (https://github.com/berlinermorgenpost/cogran)

Laden der Daten

sampled_buildings = read_rds('output/sampled_buildings.rds')
bez = readOGR('download/RBS_OD_BEZ_2015_12.geojson', layer = 'OGRGeoJSON', stringsAsFactors = FALSE)

OGR data source with driver: GeoJSON 
Source: "download/RBS_OD_BEZ_2015_12.geojson", layer: "OGRGeoJSON"
with 13 features
It has 2 fields

blk = readOGR('download/RBS_OD_BLK_2015_12.geojson', layer = 'OGRGeoJSON', stringsAsFactors = FALSE)

OGR data source with driver: GeoJSON 
Source: "download/RBS_OD_BLK_2015_12.geojson", layer: "OGRGeoJSON"
with 15720 features
It has 4 fields

lor = readOGR('download/RBS_OD_LOR_2015_12.geojson', layer = 'OGRGeoJSON', stringsAsFactors = FALSE)

OGR data source with driver: GeoJSON 
Source: "download/RBS_OD_LOR_2015_12.geojson", layer: "OGRGeoJSON"
with 447 features
It has 8 fields

re_schulstand = readOGR('download/re_schulstand.geojson', layer = 'OGRGeoJSON', stringsAsFactors = FALSE)

OGR data source with driver: GeoJSON 
Source: "download/re_schulstand.geojson", layer: "OGRGeoJSON"
with 709 features
It has 20 fields

HKO_2015 = readOGR('download/HKO_2015.geojson', 'OGRGeoJSON', stringsAsFactors = FALSE)

OGR data source with driver: GeoJSON 
Source: "download/HKO_2015.geojson", layer: "OGRGeoJSON"
with 306019 features
It has 4 fields

Schulwege

route_matrix = read_rds('output/route_matrix.rds')

Schulkapazitäten und Einwohnerzahler auf LOR-Ebene

kapas = read_csv('download/anmeldezahlen.csv') %>% filter(grepl('G', Schulnummer)) %>% filter(!is.na(`Plätze`))

Parsed with column specification:
cols(
  Bezirk = col_character(),
  Schulnummer = col_character(),
  Schulname = col_character(),
  Plätze = col_integer(),
  Anmeldungen = col_character()
)

einwohner_lor = read_delim('download/EWR201512E_Matrix.csv', delim=';')

Parsed with column specification:
cols(
  .default = col_character(),
  ZEIT = col_integer(),
  STADTRAUM = col_integer(),
  E_E = col_number(),
  E_EM = col_number(),
  E_EW = col_number(),
  E_E50_55 = col_number(),
  E_E25U55 = col_number(),
  E_E55U65 = col_number(),
  E_E65U80 = col_number()
)

See spec(...) for full column specifications.

Überprüfung der Vollständigkeit der Daten über Anmeldezahlen/Kapazitäten

re_schulstand_df = re_schulstand %>% as.data.frame() %>% rename(lon=coords.x1, lat=coords.x2) %>% cbind(over(re_schulstand, lor))
re_schulstand_df %>% filter(grepl('G', spatial_name)) %>% mutate(BEZIRK=enc2utf8(as.character(BEZIRK))) %>%
  group_by(BEZIRK) %>% summarise(`Anzahl Schulen` = n()) %>%
  rename(Bezirk=BEZIRK) %>% left_join(kapas %>% group_by(Bezirk) %>% summarise(`Mit Kapazität` = n()))

Joining, by = "Bezirk"

Für welche Bezirke haben wir für alle Schulen Kapazitäten gegeben?

re_schulstand %>% as.data.frame() %>% filter(grepl('G', spatial_name)) %>% mutate(BEZIRK=enc2utf8(as.character(BEZIRK))) %>% group_by(BEZIRK) %>% summarise(`Anzahl Schulen` = n()) %>%
  rename(Bezirk=BEZIRK) %>% left_join(kapas %>% group_by(Bezirk) %>% summarise(`Mit Kapazität` = n())) %>% filter(`Anzahl Schulen` == `Mit Kapazität`)

Joining, by = "Bezirk"

Überprüfung ob die Liste der Schulen und Liste der Schulen mit Kapazitätsinformationen gleich sind:

bezirk = 'Tempelhof-Schöneberg'
schulen_mit_kapa = kapas %>% filter(Bezirk == bezirk) %>% .$Schulnummer
schulen_mit_kapa

 [1] "07G01" "07G02" "07G03" "07G05" "07G06" "07G07" "07G10" "07G12"
 [9] "07G13" "07G14" "07G15" "07G16" "07G17" "07G18" "07G19" "07G20"
[17] "07G21" "07G22" "07G23" "07G24" "07G25" "07G26" "07G27" "07G28"
[25] "07G29" "07G30" "07G31" "07G32" "07G34" "07G35" "07G36" "07G37"

07G01

07G02

07G03

07G05

07G06

07G07

07G10

07G12

07G13

07G14

07G15

07G16

07G17

07G18

07G19

07G20

07G21

07G22

07G23

07G24

07G25

07G26

07G27

07G28

07G29

07G30

07G31

07G32

07G34

07G35

07G36

07G37

grundschulen = re_schulstand %>% as.data.frame() %>% filter(grepl('G', spatial_name)) %>% filter(BEZIRK == bezirk) %>% .$spatial_name
'In Anmeldeliste, fehlt in Schulstand'

[1] "In Anmeldeliste, fehlt in Schulstand"

In Anmeldeliste, fehlt in Schulstand

setdiff(schulen_mit_kapa, grundschulen)

character(0)

'In re_schulstand, fehlt in Anmeldeliste'

[1] "In re_schulstand, fehlt in Anmeldeliste"

In re_schulstand, fehlt in Anmeldeliste

setdiff(grundschulen, schulen_mit_kapa)

character(0)

map = get_map('Berlin')

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=Berlin&zoom=10&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Berlin&sensor=false

re_schulstand_df_w_kapas = re_schulstand_df %>% left_join(kapas, by=c('spatial_name'='Schulnummer'))

Plot aller Schulen, mit der Info, ob Kapazitätsinformationen verfügbar sind.

data = re_schulstand_df_w_kapas %>% filter(grepl('G', spatial_name)) %>% filter(BEZIRK==bezirk) %>% mutate(missing.capa=is.na(`Plätze`))
ggmap(map) + geom_point(aes(lon, lat, color=missing.capa), data=data) +
    coord_map(xlim=c(min(data$lon)-0.01, max(data$lon)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01))

Filter auf Schulen mit Kapazitätsinformationen (für T-S sind das alle):

relevant_schools = re_schulstand_df_w_kapas %>% filter(grepl('G', spatial_name)) %>% filter(BEZIRK==bezirk & !is.na(`Plätze`)) %>% .$spatial_name
relevant_schools

 [1] "07G01" "07G02" "07G03" "07G05" "07G06" "07G07" "07G10" "07G12"
 [9] "07G13" "07G14" "07G15" "07G16" "07G17" "07G18" "07G19" "07G20"
[17] "07G21" "07G22" "07G23" "07G24" "07G25" "07G26" "07G27" "07G28"
[25] "07G29" "07G30" "07G31" "07G32" "07G34" "07G35" "07G36" "07G37"

07G01

07G02

07G03

07G05

07G06

07G07

07G10

07G12

07G13

07G14

07G15

07G16

07G17

07G18

07G19

07G20

07G21

07G22

07G23

07G24

07G25

07G26

07G27

07G28

07G29

07G30

07G31

07G32

07G34

07G35

07G36

07G37

Mapping Bezirk->LOR->Block

df_bez = as.data.frame(bez)
df_lor = as.data.frame(lor)
df_blk = as.data.frame(blk)

Sanity-Check: LORs und Blöcke im Bezirk

bez_id = filter(df_bez, BEZNAME == bezirk)$BEZ
relevant_lors = df_lor %>% filter(BEZ == bez_id)
relevant_blks = df_blk %>% filter(BEZ == bez_id)

ggplot() + geom_path(aes(x=long, y=lat, group=group), data=lor[lor$BEZ == bez_id,]) + coord_map() + geom_path(aes(x=long, y=lat, group=group), data=bez, color='red')

Regions defined for each Polygons
Regions defined for each Polygons

Blöcke im Bezirk

ggplot() + geom_path(aes(x=long, y=lat, group=group), data=blk[blk$BEZ == bez_id,]) + coord_map() + geom_path(aes(x=long, y=lat, group=group), data=bez[bez$BEZ == bez_id,], color='red')

Regions defined for each Polygons
Regions defined for each Polygons

Kinder im Bezirk auf Blöcke hochrechnen

Über die Einwohnerinformationen in RBS_OD_BLK_2015_12.geojson kann EWR201512E_Matrix.csv von LOR-Ebene auf Blockebene hochgerechnet werden.

TODO: Stattdessen mit https://github.com/berlinermorgenpost/cogran machen?

Plot der 6-Jährigen nach EWR201512E_Matrix.csv

Wir verwenden das mittel der 5- und 6-Jährigen.

TODO neue Daten von Torres? TODO Prognose?

relevant_ewr = einwohner_lor %>%
  select(RAUMID, E_E05_06, E_E06_07) %>%
  filter(RAUMID %in% relevant_lors$PLR) %>%
  # Schnitt der 5 und 6-Jährigen
  mutate(kids=(as.numeric(gsub(',','.',E_E06_07))+as.numeric(gsub(',','.',E_E05_06)))/2) %>% as.data.frame()

data = tidy(lor[lor$BEZ == bez_id,], region='PLR') %>% inner_join(relevant_ewr, by=c('id'='RAUMID'))
ggmap(map) + geom_polygon(aes(x=long, y=lat, group=group, fill=kids), data=data) +
  coord_map(xlim=c(min(data$long)-0.01, max(data$long)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01))

Plot der Einwohner auf Blockebene

data = tidy(blk[blk$BEZ == bez_id,], region='BLK') %>% inner_join(df_blk, by=c('id'='BLK')) %>% mutate(Einw=ifelse(Einw==0, NA, Einw))
0

[1] 0

ggmap(map) + geom_polygon(aes(x=long, y=lat, group=group, fill=Einw), data=data) +
  coord_map(xlim=c(min(data$long)-0.01, max(data$long)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01))

Hochrrechnung auf Blöcke, Strukturquote

Strukturquote = 0.9

kids_in_blks = relevant_blks %>%
  group_by(PLR) %>%
  mutate(EinwRatio = Einw/sum(Einw)) %>%
  ungroup %>%
  left_join(relevant_ewr, by=c('PLR'='RAUMID')) %>%
  mutate(kids = EinwRatio*kids) %>%
  mutate(kids = Strukturquote*kids) %>% # Strukturquote
  select(BEZ, PLR, BLK, Einw, kids) %>%
  as.data.frame()
row.names(kids_in_blks) = kids_in_blks$BLK

data = tidy(blk[blk$BEZ == bez_id,], region='BLK') %>% inner_join(kids_in_blks, by=c('id'='BLK')) %>% mutate(kids=ifelse(kids==0, NA, kids), Einw=ifelse(Einw==0, NA, Einw))

ggmap(map) + geom_polygon(aes(x=long, y=lat, group=group, fill=kids), data=data) +
  coord_map(xlim=c(min(data$long)-0.01, max(data$long)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01))

Verfügbare Plätze

relevant_kapas = kapas %>% select(Schulnummer, Kapa=`Plätze`) %>% filter(Schulnummer %in% relevant_schools) %>% as.data.frame()
#row.names(relevant_kapas) = relevant_kapas$Schulnummer

Überprüfung der Summe der Kapazitäten, Anmeldungen und Kinderstatistiken

'Summe Kapas'

[1] "Summe Kapas"

Summe Kapas

relevant_kapas %>% .$Kapa %>% sum

[1] 2584

'Anmeldungen'

[1] "Anmeldungen"

Anmeldungen

kapas %>% mutate(Anmeldungen = as.numeric(gsub('[^0-9]', '', Anmeldungen))) %>% filter(Schulnummer %in% relevant_schools) %>% .$Anmeldungen %>% sum

[1] 2752

'Kids laut Statistik'

[1] "Kids laut Statistik"

Kids laut Statistik

kids_in_blks$kids %>% sum

[1] 2620.8

relevant_ewr$kids %>% sum

[1] 2912

Schulwege von Blöcken zu Schulen aggregieren

Für jedes Wohngebäude suchen wir den zugehörigen Block

residential_buildings_blocks = sampled_buildings %>% inner_join(df_blk) %>% filter(BEZ == bez_id)

Joining, by = "BLK"

residential_buildings_blocks

routes_from_blks = residential_buildings_blocks %>%
  left_join(route_matrix %>% filter(dst %in% relevant_schools), by=c('OI'='src'))
head(routes_from_blks)

Plot der relevanten Blöcke (mit Wohngebäuden)

data = tidy(blk[blk$BEZ == bez_id,], region='BLK') %>% inner_join(routes_from_blks %>% group_by(BLK) %>% summarise(n=n()), by=c('id'='BLK'))
ggmap(map) + geom_polygon(aes(x=long, y=lat, group=group), fill='red', data=data) +
  #geom_point(aes(x=lon, y=lat), data=rb_df, color='black', size=0.01) +
  coord_map(xlim=c(min(data$long)-0.01, max(data$long)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01))

travel_from_blks = routes_from_blks %>% as.data.frame() %>% group_by(BLK, dst) %>% summarise(min=min(distance), avg=mean(distance), med=median(distance), max=max(distance)) %>% ungroup
travel_from_blks

Plot der Blöcke mit Färbung nach durchschnittlichem Weg zur nächsten Schule

data = tidy(blk[blk$BEZ == bez_id,], region='BLK') %>% left_join(travel_from_blks %>% group_by(BLK) %>% top_n(1, -avg), by=c('id'='BLK'))
ggmap(map) + geom_polygon(aes(x=long, y=lat, group=group, fill=-avg), data=data) +
  geom_point(aes(lon, lat), color='red', data = re_schulstand_df %>% filter(BEZIRK==bezirk & SCHULART=='Grundschule')) +
  coord_map(xlim=c(min(data$long)-0.01, max(data$long)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01))

travel_matrix = travel_from_blks %>% select(BLK, dst, avg) %>% spread(dst, avg)
dim(travel_matrix)

[1] 1012   33

travel_matrix

Sozioökonomische Daten

Wir haben Sozioökonomische Daten in den Wahlbezirken. Strategie: - Schneiden der Wahlbezirke mit den Blöcken - Übernahme des Prozentwertes vom Wahlbezirk für jeden (Unter-)block - Zuordnung zu jedem Block und Vereinigung durch Flächen/Wohnhaus-gewichtetes Mittel des Prozentwertes

UWB = readOGR('download/RBS_OD_UWB_AGH2016.geojson', layer = 'OGRGeoJSON', stringsAsFactors = FALSE)

OGR data source with driver: GeoJSON 
Source: "download/RBS_OD_UWB_AGH2016.geojson", layer: "OGRGeoJSON"
with 1779 features
It has 4 fields

UWB$ID = paste0(UWB$BEZ, UWB$UWB)
sozio_UWB = read_excel('download/DL_BE_AH2016_Strukturdaten.xlsx', sheet = 3) %>% select(ID, sgbIIu65=`Einwohner unter 65 in SGB II 2014 Prozent`)
UWB = UWB %>% sp::merge(sozio_UWB, by='ID')

ggplot(broom::tidy(UWB, region='ID') %>% inner_join(UWB %>% as.data.frame, by=c('id'='ID'))) + geom_polygon(aes(x=long, y=lat, group=group, fill=sgbIIu65)) + coord_map()

Check for self intersections

wgs84 = CRS(proj4string(blk))
ea_projection = CRS("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")
blk_in_bez = blk[blk$BEZ == bez_id,]
gIsValid(blk_in_bez)

[1] TRUE

blk_in_bez = gBuffer(spTransform(blk_in_bez, ea_projection), byid=T, width = -0.1)
any(xor(gIntersects(blk_in_bez, byid=T, returnDense = T), diag(1, length(blk_in_bez)) == 1))

[1] FALSE

plot(blk_in_bez)

#gIntersects(UWB[UWB$BEZ == '07',], byid=TRUE)

uwb_in_bez = gBuffer(spTransform(UWB[UWB$BEZ == '07',], ea_projection), byid=T, width=-0.1)
gIsValid(uwb_in_bez)

[1] TRUE

any(xor(gIntersects(uwb_in_bez, byid=T, returnDense = T), diag(1, length(uwb_in_bez)) == 1))

[1] FALSE

plot(uwb_in_bez)

intersection = gIntersection(uwb_in_bez, blk_in_bez, byid = T, drop_lower_td = T)

gIsValid(intersection)

[1] TRUE

plot(intersection)

intersection_uwb_data = intersection %>% over(uwb_in_bez)
intersection_blk_data = intersection %>% over(blk_in_bez)
intersection_area = gArea(intersection, byid=T)
intersection_data = cbind(
    intersection_uwb_data %>% select(UWB_ID=ID, sgbIIu65),
    intersection_blk_data %>% select(BLK, BLK_Einw=Einw),
    data.frame(area=intersection_area) # FIXME how else to normalize?
    )

length(intersection)

[1] 1219

nrow(blk_in_bez)

[1] 1201

# did we miss any blocks?
setdiff(blk_in_bez$BLK, intersection_data$BLK)

character(0)

Pro Block mische die SGB-Werte der Unterblöcke gewichtet nach Fläche. FIXME - besser nach Anzahl der Wohnhäuser?

sgbII_blk = intersection_data %>%
  group_by(BLK) %>%
  summarise(sgbIIu65=sum(sgbIIu65*area)/sum(area)/100)

ggplot(broom::tidy(spTransform(blk_in_bez, wgs84), region='BLK') %>% left_join(sgbII_blk, by=c('id'='BLK'))) + geom_polygon(aes(x=long, y=lat, group=group, fill=sgbIIu65)) + coord_map()

Adjazenz von Blöcken

row.names(blk_in_bez) = blk_in_bez$BLK
buffeded_blk_in_bez = gBuffer(blk_in_bez, byid=T, width=40)
adjacency = gIntersects(gBuffer(blk_in_bez, byid=T, width=40), byid = T)
rownames(adjacency) = blk_in_bez$BLK
colnames(adjacency) = blk_in_bez$BLK

adjacency_df = adjacency %>% as.data.frame() %>% mutate(from=rownames(.)) %>%
  gather(to, connected, -from) %>% filter(connected) %>%
  inner_join(spTransform(blk_in_bez, wgs84) %>% coordinates() %>% as.data.frame() %>% rename(from_long=V1, from_lat=V2) %>% mutate(from=rownames(.))) %>%
  inner_join(spTransform(blk_in_bez, wgs84) %>% coordinates() %>% as.data.frame() %>% rename(to_long=V1, to_lat=V2) %>% mutate(to=rownames(.)))

Joining, by = "from"

Joining, by = "to"

ggplot() +
  geom_polygon(aes(x=long, y=lat, group=group), fill='gray', data=broom::tidy(spTransform(blk_in_bez, wgs84), region='BLK')) + 
  geom_segment(aes(x=from_long, y=from_lat, xend=to_long, yend=to_lat), size=0.1, color='black', data=adjacency_df) +
  theme_nothing() + coord_map()

Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
instead

ggsave('figs/adjacency.pdf')

Saving 7 x 5 in image

adjacency_df %>% filter(connected & from != to) %>% select(from, to) %>% write_csv('app/data/adjacency.csv')

Select relevant data

optim_kapas = relevant_kapas
optim_kids_in_blks = kids_in_blks %>% filter(kids > 0) %>% inner_join(travel_matrix, by='BLK') %>% select(BLK, kids) %>% mutate(kids=kids)
nrow(optim_kids_in_blks)

[1] 1012

nrow(optim_kapas)

[1] 32

select_schools = as.character(optim_kapas$Schulnummer)
select_blks = as.character(optim_kids_in_blks$BLK)

optim_matrix = inner_join(optim_kids_in_blks, travel_matrix, by='BLK')[select_schools]

dim(optim_matrix)

[1] 1012   32

optim_kapas$Kapa %>% sum

[1] 2584

optim_kids_in_blks$kids %>% sum

[1] 2611.995

Naive Zuordnung: Jeder Block zur nächsten Schule

solution = optim_matrix %>% mutate(BLK=optim_kids_in_blks$BLK) %>% gather(school, dist, -BLK) %>% group_by(BLK) %>% top_n(1, -dist) %>% ungroup

optim_matrix %>% t %>% as.data.frame %>% summarise_each(funs(min)) %>% sum()

[1] 781609.6

solines = re_schulstand_df %>% inner_join(solution, by=c('spatial_name'='school')) %>% inner_join(cbind(as.data.frame(coordinates(blk[blk$BEZ == bez_id,])), blk[blk$BEZ == bez_id,]@data['BLK']))

Joining, by = "BLK"

data = tidy(blk[blk$BEZ == bez_id,], region='BLK') %>% left_join(solution, by=c('id'='BLK'))
ggmap(map, darken = c(0.5, 'white')) + geom_polygon(aes(x=long, y=lat, group=group, fill=school), data=data) +
  geom_segment(aes(x=V1,y=V2,xend=lon,yend=lat), data=solines, size=0.3) +
  geom_point(aes(lon, lat), color='black', size=2, data = re_schulstand_df %>% inner_join(solution, by=c('spatial_name'='school'))) +
  geom_point(aes(lon, lat, color=spatial_name), data = re_schulstand_df %>% inner_join(solution, by=c('spatial_name'='school'))) +
  coord_map(xlim=c(min(data$long)-0.01, max(data$long)+0.01), ylim=c(min(data$lat)-0.01, max(data$lat)+0.01)) +
  guides(color=F, fill=F)

Darstellung der Zuordnung als Tabelle

library(formattable)

solution %>% inner_join(optim_kids_in_blks, by='BLK') %>% inner_join(travel_from_blks, by=c('BLK'='BLK', 'school'='dst')) %>%
  group_by(school) %>% summarise(
    kids=sum(kids),
    num_blocks=n(),
    min_dist=min(min),
    avg_dist=mean((kids*avg)/sum(kids)),
    max_dist=max(max)
  ) %>%
  inner_join(relevant_kapas, by=c('school'='Schulnummer')) %>%
  mutate(
    utilization=kids/Kapa
  ) %>% select(
   Schule=school,
   `Blöcke`=num_blocks,
   Kapazität=Kapa,
   Kinder=kids,
   Auslastung=utilization,
   `Weg (min)`=min_dist,
   `Weg (Ø)`=avg_dist,
   `Weg (max)`=max_dist
  ) %>%
  formattable(
    list(
      Kinder = formatter("span", x ~ digits(x, 2)),
      Auslastung = formatter("span",
        style = x ~ style(color = ifelse(x < 1, "green", "red")),
        x ~ icontext(ifelse(x < 1, "ok", "remove"), percent(x))
      ),
      `Weg (Ø)` = proportion_bar("lightblue"),
      `Weg (min)` = proportion_bar("lightblue"),
      `Weg (max)` = proportion_bar("lightblue")
    )
  )

Schule	Blöcke	Kapazität	Kinder	Auslastung	Weg (min)	Weg (Ø)	Weg (max)
07G01	10	66	49.42	74.87%	287.933136	603.5498	1157.8849
07G02	19	98	43.55	44.44%	59.205467	435.8940	896.3418
07G03	14	73	56.66	77.61%	70.582603	411.5156	1037.2817
07G05	19	78	90.09	115.50%	73.309372	538.3502	962.0107
07G06	29	55	79.84	145.17%	241.365601	806.7650	1291.6294
07G07	10	81	16.10	19.88%	159.838760	585.8896	1359.2705
07G10	32	112	152.98	136.59%	88.422501	656.5627	1520.3507
07G12	12	75	33.54	44.72%	29.084946	360.0576	646.2440
07G13	28	82	133.36	162.63%	6.723136	508.4283	999.8708
07G14	32	78	76.44	98.00%	111.006302	438.5092	779.3000
07G15	49	104	209.08	201.04%	87.995552	829.5834	1612.8036
07G16	21	104	55.63	53.49%	139.662811	577.2329	1010.9780
07G17	24	100	70.58	70.58%	51.495129	419.9221	713.7672
07G18	20	44	65.93	149.83%	67.150185	358.2698	642.9683
07G19	32	112	97.54	87.09%	116.220581	1114.9476	2214.6074
07G20	48	81	160.35	197.97%	75.543091	684.0351	1458.9825
07G21	23	72	69.54	96.58%	180.236908	499.9357	1312.0090
07G22	28	91	70.37	77.33%	136.316772	630.6502	1490.5638
07G23	9	50	21.91	43.82%	188.932098	732.4195	1194.2965
07G24	26	75	70.58	94.11%	164.014404	614.9176	1473.8057
07G25	36	75	122.51	163.35%	40.946930	592.4875	1160.8953
07G26	26	52	35.74	68.73%	129.166183	583.6184	1078.1425
07G27	19	75	46.20	61.60%	38.674854	691.0156	1295.4709
07G28	60	75	81.88	109.17%	119.915337	961.6523	1791.4529
07G29	95	104	113.15	108.80%	16.785637	966.7198	2026.0449
07G30	44	72	75.78	105.25%	135.504837	875.0386	2417.1389
07G31	20	78	46.84	60.05%	176.327759	851.4545	1529.7749
07G32	60	78	55.53	71.19%	190.077850	738.2852	1332.8569
07G34	41	100	178.11	178.11%	156.965714	1145.5052	2229.8320
07G35	34	75	88.26	117.68%	130.356995	764.2659	1534.7168
07G36	35	100	56.39	56.39%	190.601837	1010.8961	2341.1589
07G37	57	69	88.14	127.75%	2.115040	1243.0577	2116.3857

Daten für die App speichern

• entities.geojson
• entities.csv
• units.geojson
• units.csv
• weights.csv
• assignment.csv

Neue Daten

Entities / Schulen

entities = subset(re_schulstand_df, spatial_name %in% relevant_schools) %>%
  rename(entity_id = spatial_name) %>%
  select(-gml_id, -spatial_alias, -spatial_type) %>%
  inner_join(rename(relevant_kapas, capacity=Kapa), by=c('entity_id'='Schulnummer'))
coordinates(entities) = ~ lon + lat
proj4string(entities) = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
file.remove('app/data/entities.geojson')

[1] TRUE

entities %>% writeOGR('app/data/entities.geojson', layer="entities", driver="GeoJSON", check_exists=F)
entities@data %>% select(entity_id, capacity) %>% write_csv('app/data/entities.csv')

Units / Statistische Blöcke

units = subset(blk, BEZ == bez_id)

units@data$PLR = NULL
units@data$Einw = NULL
units@data$BEZ = NULL
units@data$unit_id = units@data$BLK
units@data$BLK = NULL

units = units %>% sp::merge(
  optim_kids_in_blks %>%
    left_join(sgbII_blk) %>%
    select(unit_id=BLK, population=kids, sgbIIu65)
  )

Joining, by = "BLK"

file.remove('app/data/units.geojson')

[1] TRUE

units %>% writeOGR('app/data/units.geojson', layer="units", driver="GeoJSON", check_exists=F)
units@data %>% write_csv('app/data/units.csv')

Zuordnung

assignment = solution %>% select(unit_id=BLK, entity_id=school)
assignment %>% write_csv('app/data/assignment.csv')

Weights / Schulwege

travel_from_blks %>%
  rename(unit_id=BLK, entity_id=dst) %>%
  #gather(weight, value, -unit_id, -entity_id) %>%
  write_csv('app/data/weights.csv')

Zusätzliche Daten

file.copy('download/RBS_OD_BEZ_2015_12.geojson', 'app/data/RBS_OD_BEZ_2015_12.geojson')

[1] FALSE

addresses_in_bez = HKO_2015[!is.na(over(HKO_2015, subset(blk, BEZ == bez_id))$BEZ),c('STN', 'HNR')]
names(addresses_in_bez) = c('street', 'no')
addresses_in_bez@data

file.remove('app/data/addresses.geojson')

[1] TRUE

writeOGR(addresses_in_bez, "app/data/addresses.geojson", layer="addresses", driver="GeoJSON", check_exists = FALSE)